Research Assistant: AstraZeneca's Agentic System for R&D

2026-08-06

AI Summary

This work addresses the challenge of efficiently integrating heterogeneous, multi-source data in biomedical research by proposing and implementing an intelligent scientific assistant powered by large language models. The system unifies multimodal data—including scientific literature, knowledge graphs, chemical databases, and clinical trial records—through semantic retrieval, enabling both question-answering and multi-step reasoning interactions. It incorporates an evidence-tracing mechanism to ensure interpretability and auditability of its outputs. As the first system to achieve cross-source semantic integration and traceable reasoning in pharmaceutical R&D, it has been deployed across AstraZeneca’s global research infrastructure, significantly enhancing researchers’ information retrieval efficiency and their capacity for automated exploration of complex drug discovery tasks.

Abstract

We describe Research Assistant, an internal LLM-based system developed at AstraZeneca to help scientists and clinicians explore biomedical questions across a broad range of data sources. The system provides a chat-style interface that brings together evidence from scientific literature, knowledge graphs, chemistry, clinical trials, safety resources, expression data, and internal experimental systems. It supports both a fast mode for direct question answering and a multi-step mode for more complex research tasks. Responses are grounded in retrieved evidence and linked back to the original sources, allowing users to review and further explore the underlying data. In this technical note, we outline the system architecture, the main design choices behind the product, and lessons learned from deploying it at scale to support day-to-day R&D workflows across AstraZeneca.

Problem

• biomedical research
• data integration
• evidence retrieval
• R&D support
• scientific question answering

Innovation

• Agentic AI
• Retrieval-Augmented Generation
• Multimodal Biomedical Integration
• Evidence Grounding
• R&D Workflow Automation